Overview

Dataset statistics

Number of variables45
Number of observations899
Missing cells11432
Missing cells (%)28.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory368.1 KiB
Average record size in memory419.3 B

Variable types

Numeric20
Categorical25

Alerts

age is highly correlated with years and 2 other fieldsHigh correlation
painloc is highly correlated with cp and 1 other fieldsHigh correlation
painexer is highly correlated with relrest and 3 other fieldsHigh correlation
relrest is highly correlated with painexer and 2 other fieldsHigh correlation
cp is highly correlated with painloc and 4 other fieldsHigh correlation
trestbps is highly correlated with trestbpdHigh correlation
chol is highly correlated with met and 3 other fieldsHigh correlation
smoke is highly correlated with cigs and 1 other fieldsHigh correlation
cigs is highly correlated with smoke and 4 other fieldsHigh correlation
years is highly correlated with age and 2 other fieldsHigh correlation
fbs is highly correlated with exerwmHigh correlation
famhist is highly correlated with exerefHigh correlation
prop is highly correlated with restefHigh correlation
nitr is highly correlated with pro and 1 other fieldsHigh correlation
pro is highly correlated with nitr and 1 other fieldsHigh correlation
proto is highly correlated with restecg and 5 other fieldsHigh correlation
thaldur is highly correlated with proto and 4 other fieldsHigh correlation
thaltime is highly correlated with proto and 4 other fieldsHigh correlation
met is highly correlated with chol and 2 other fieldsHigh correlation
thalach is highly correlated with thaldur and 4 other fieldsHigh correlation
thalrest is highly correlated with thalach and 1 other fieldsHigh correlation
tpeakbps is highly correlated with xhypoHigh correlation
tpeakbpd is highly correlated with restefHigh correlation
trestbpd is highly correlated with trestbps and 2 other fieldsHigh correlation
exang is highly correlated with painexer and 5 other fieldsHigh correlation
oldpeak is highly correlated with thaltime and 2 other fieldsHigh correlation
slope is highly correlated with exang and 1 other fieldsHigh correlation
rldv5 is highly correlated with rldv5e and 1 other fieldsHigh correlation
rldv5e is highly correlated with restecg and 2 other fieldsHigh correlation
ca is highly correlated with painloc and 2 other fieldsHigh correlation
restef is highly correlated with painexer and 11 other fieldsHigh correlation
restwm is highly correlated with dig and 3 other fieldsHigh correlation
exeref is highly correlated with age and 20 other fieldsHigh correlation
exerwm is highly correlated with age and 7 other fieldsHigh correlation
num is highly correlated with oldpeak and 2 other fieldsHigh correlation
cathef is highly correlated with chol and 4 other fieldsHigh correlation
dm is highly correlated with ageHigh correlation
restecg is highly correlated with proto and 1 other fieldsHigh correlation
dig is highly correlated with restef and 1 other fieldsHigh correlation
xhypo is highly correlated with tpeakbpsHigh correlation
thal is highly correlated with restefHigh correlation
dataset is highly correlated with chol and 7 other fieldsHigh correlation
painloc has 282 (31.4%) missing values Missing
painexer has 282 (31.4%) missing values Missing
relrest has 286 (31.8%) missing values Missing
trestbps has 59 (6.6%) missing values Missing
htn has 34 (3.8%) missing values Missing
chol has 30 (3.3%) missing values Missing
smoke has 669 (74.4%) missing values Missing
cigs has 420 (46.7%) missing values Missing
years has 432 (48.1%) missing values Missing
fbs has 90 (10.0%) missing values Missing
dm has 804 (89.4%) missing values Missing
famhist has 422 (46.9%) missing values Missing
dig has 68 (7.6%) missing values Missing
prop has 66 (7.3%) missing values Missing
nitr has 65 (7.2%) missing values Missing
pro has 63 (7.0%) missing values Missing
diuretic has 82 (9.1%) missing values Missing
proto has 112 (12.5%) missing values Missing
thaldur has 56 (6.2%) missing values Missing
thaltime has 453 (50.4%) missing values Missing
met has 105 (11.7%) missing values Missing
thalach has 55 (6.1%) missing values Missing
thalrest has 56 (6.2%) missing values Missing
tpeakbps has 63 (7.0%) missing values Missing
tpeakbpd has 63 (7.0%) missing values Missing
trestbpd has 59 (6.6%) missing values Missing
exang has 55 (6.1%) missing values Missing
xhypo has 58 (6.5%) missing values Missing
oldpeak has 62 (6.9%) missing values Missing
slope has 308 (34.3%) missing values Missing
rldv5 has 425 (47.3%) missing values Missing
rldv5e has 142 (15.8%) missing values Missing
ca has 608 (67.6%) missing values Missing
restef has 871 (96.9%) missing values Missing
restwm has 869 (96.7%) missing values Missing
exeref has 897 (99.8%) missing values Missing
exerwm has 894 (99.4%) missing values Missing
thal has 477 (53.1%) missing values Missing
cathef has 588 (65.4%) missing values Missing
exeref is uniformly distributed Uniform
chol has 172 (19.1%) zeros Zeros
cigs has 153 (17.0%) zeros Zeros
years has 153 (17.0%) zeros Zeros
thaltime has 70 (7.8%) zeros Zeros
oldpeak has 362 (40.3%) zeros Zeros

Reproduction

Analysis started2022-11-22 07:28:25.356481
Analysis finished2022-11-22 07:28:55.774264
Duration30.42 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.48053393
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:55.824801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q147
median54
Q360
95-th percentile68
Maximum77
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.435893735
Coefficient of variation (CV)0.1764360421
Kurtosis-0.3806810233
Mean53.48053393
Median Absolute Deviation (MAD)7
Skewness-0.1831893834
Sum48079
Variance89.03609058
MonotonicityNot monotonic
2022-11-22T08:28:55.908616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5451
 
5.7%
5840
 
4.4%
5539
 
4.3%
5236
 
4.0%
5636
 
4.0%
5735
 
3.9%
6235
 
3.9%
5135
 
3.9%
5934
 
3.8%
5333
 
3.7%
Other values (40)525
58.4%
ValueCountFrequency (%)
281
 
0.1%
293
 
0.3%
301
 
0.1%
312
 
0.2%
325
0.6%
332
 
0.2%
347
0.8%
3510
1.1%
366
0.7%
3711
1.2%
ValueCountFrequency (%)
772
 
0.2%
762
 
0.2%
753
 
0.3%
747
0.8%
731
 
0.1%
724
 
0.4%
715
 
0.6%
707
0.8%
6913
1.4%
689
1.0%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
1
711 
0
188 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1711
79.1%
0188
 
20.9%

Length

2022-11-22T08:28:55.979519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:56.037392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1711
79.1%
0188
 
20.9%

Most occurring characters

ValueCountFrequency (%)
1711
79.1%
0188
 
20.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1711
79.1%
0188
 
20.9%

Most occurring scripts

ValueCountFrequency (%)
Common899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1711
79.1%
0188
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1711
79.1%
0188
 
20.9%

painloc
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing282
Missing (%)31.4%
Memory size47.3 KiB
1.0
568 
0.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1851
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0568
63.2%
0.049
 
5.5%
(Missing)282
31.4%

Length

2022-11-22T08:28:56.087172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:56.142857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0568
92.1%
0.049
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0666
36.0%
.617
33.3%
1568
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1234
66.7%
Other Punctuation617
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0666
54.0%
1568
46.0%
Other Punctuation
ValueCountFrequency (%)
.617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1851
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0666
36.0%
.617
33.3%
1568
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0666
36.0%
.617
33.3%
1568
30.7%

painexer
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing282
Missing (%)31.4%
Memory size47.3 KiB
1.0
366 
0.0
251 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1851
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0366
40.7%
0.0251
27.9%
(Missing)282
31.4%

Length

2022-11-22T08:28:56.189689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:56.245362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0366
59.3%
0.0251
40.7%

Most occurring characters

ValueCountFrequency (%)
0868
46.9%
.617
33.3%
1366
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1234
66.7%
Other Punctuation617
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0868
70.3%
1366
29.7%
Other Punctuation
ValueCountFrequency (%)
.617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1851
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0868
46.9%
.617
33.3%
1366
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0868
46.9%
.617
33.3%
1366
19.8%

relrest
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing286
Missing (%)31.8%
Memory size47.2 KiB
1.0
412 
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1839
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0412
45.8%
0.0201
22.4%
(Missing)286
31.8%

Length

2022-11-22T08:28:56.293485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:56.349690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0412
67.2%
0.0201
32.8%

Most occurring characters

ValueCountFrequency (%)
0814
44.3%
.613
33.3%
1412
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1226
66.7%
Other Punctuation613
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0814
66.4%
1412
33.6%
Other Punctuation
ValueCountFrequency (%)
.613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1839
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0814
44.3%
.613
33.3%
1412
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0814
44.3%
.613
33.3%
1412
22.4%

cp
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
4
485 
3
202 
2
167 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4485
53.9%
3202
22.5%
2167
 
18.6%
145
 
5.0%

Length

2022-11-22T08:28:56.406195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:56.473174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4485
53.9%
3202
22.5%
2167
 
18.6%
145
 
5.0%

Most occurring characters

ValueCountFrequency (%)
4485
53.9%
3202
22.5%
2167
 
18.6%
145
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4485
53.9%
3202
22.5%
2167
 
18.6%
145
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4485
53.9%
3202
22.5%
2167
 
18.6%
145
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4485
53.9%
3202
22.5%
2167
 
18.6%
145
 
5.0%

trestbps
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)7.1%
Missing59
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean132.1011905
Minimum0
Maximum200
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:56.540184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range200
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.15112717
Coefficient of variation (CV)0.1449731611
Kurtosis2.968612092
Mean132.1011905
Median Absolute Deviation (MAD)10
Skewness0.2075650027
Sum110965
Variance366.765672
MonotonicityNot monotonic
2022-11-22T08:28:56.618216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120128
14.2%
130112
12.5%
140100
 
11.1%
11058
 
6.5%
15056
 
6.2%
16050
 
5.6%
12528
 
3.1%
11519
 
2.1%
13518
 
2.0%
12816
 
1.8%
Other values (50)255
28.4%
(Missing)59
 
6.6%
ValueCountFrequency (%)
01
 
0.1%
801
 
0.1%
921
 
0.1%
942
 
0.2%
956
 
0.7%
961
 
0.1%
981
 
0.1%
10015
1.7%
1011
 
0.1%
1023
 
0.3%
ValueCountFrequency (%)
2004
 
0.4%
1921
 
0.1%
1902
 
0.2%
1851
 
0.1%
18012
1.3%
1783
 
0.3%
1741
 
0.1%
1722
 
0.2%
17013
1.4%
1652
 
0.2%

htn
Categorical

MISSING

Distinct2
Distinct (%)0.2%
Missing34
Missing (%)3.8%
Memory size52.1 KiB
0.0
453 
1.0
412 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2595
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0453
50.4%
1.0412
45.8%
(Missing)34
 
3.8%

Length

2022-11-22T08:28:56.689885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:56.746485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0453
52.4%
1.0412
47.6%

Most occurring characters

ValueCountFrequency (%)
01318
50.8%
.865
33.3%
1412
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1730
66.7%
Other Punctuation865
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01318
76.2%
1412
 
23.8%
Other Punctuation
ValueCountFrequency (%)
.865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2595
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01318
50.8%
.865
33.3%
1412
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01318
50.8%
.865
33.3%
1412
 
15.9%

chol
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct213
Distinct (%)24.5%
Missing30
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean198.7594937
Minimum0
Maximum603
Zeros172
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:56.806936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1175
median224
Q3269
95-th percentile334.2
Maximum603
Range603
Interquartile range (IQR)94

Descriptive statistics

Standard deviation111.8344148
Coefficient of variation (CV)0.5626620028
Kurtosis0.00684770048
Mean198.7594937
Median Absolute Deviation (MAD)46
Skewness-0.6049884266
Sum172722
Variance12506.93633
MonotonicityNot monotonic
2022-11-22T08:28:56.885417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
 
19.1%
25410
 
1.1%
2049
 
1.0%
2199
 
1.0%
2309
 
1.0%
2169
 
1.0%
2239
 
1.0%
2209
 
1.0%
2119
 
1.0%
2608
 
0.9%
Other values (203)616
68.5%
(Missing)30
 
3.3%
ValueCountFrequency (%)
0172
19.1%
851
 
0.1%
1002
 
0.2%
1171
 
0.1%
1261
 
0.1%
1291
 
0.1%
1321
 
0.1%
1391
 
0.1%
1411
 
0.1%
1421
 
0.1%
ValueCountFrequency (%)
6031
0.1%
5641
0.1%
5291
0.1%
5181
0.1%
4911
0.1%
4681
0.1%
4661
0.1%
4581
0.1%
4171
0.1%
4121
0.1%

smoke
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.9%
Missing669
Missing (%)74.4%
Memory size39.7 KiB
1.0
119 
0.0
111 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters690
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0119
 
13.2%
0.0111
 
12.3%
(Missing)669
74.4%

Length

2022-11-22T08:28:56.955923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:57.010722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0119
51.7%
0.0111
48.3%

Most occurring characters

ValueCountFrequency (%)
0341
49.4%
.230
33.3%
1119
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number460
66.7%
Other Punctuation230
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0341
74.1%
1119
 
25.9%
Other Punctuation
ValueCountFrequency (%)
.230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common690
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0341
49.4%
.230
33.3%
1119
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0341
49.4%
.230
33.3%
1119
 
17.2%

cigs
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct25
Distinct (%)5.2%
Missing420
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean19.11899791
Minimum0
Maximum99
Zeros153
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:57.061899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q330
95-th percentile51
Maximum99
Range99
Interquartile range (IQR)30

Descriptive statistics

Standard deviation18.29627263
Coefficient of variation (CV)0.9569681799
Kurtosis0.6835763697
Mean19.11899791
Median Absolute Deviation (MAD)20
Skewness0.8929868778
Sum9158
Variance334.7535923
MonotonicityNot monotonic
2022-11-22T08:28:57.131703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0153
 
17.0%
20136
 
15.1%
4063
 
7.0%
3035
 
3.9%
1021
 
2.3%
6017
 
1.9%
5012
 
1.3%
159
 
1.0%
26
 
0.7%
256
 
0.7%
Other values (15)21
 
2.3%
(Missing)420
46.7%
ValueCountFrequency (%)
0153
17.0%
11
 
0.1%
26
 
0.7%
32
 
0.2%
42
 
0.2%
52
 
0.2%
71
 
0.1%
81
 
0.1%
91
 
0.1%
1021
 
2.3%
ValueCountFrequency (%)
991
 
0.1%
803
 
0.3%
751
 
0.1%
701
 
0.1%
651
 
0.1%
6017
 
1.9%
5012
 
1.3%
451
 
0.1%
4063
7.0%
352
 
0.2%

years
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct42
Distinct (%)9.0%
Missing432
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean18.79657388
Minimum0
Maximum60
Zeros153
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:57.220203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q330
95-th percentile45
Maximum60
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.35914482
Coefficient of variation (CV)0.8703258862
Kurtosis-1.258896079
Mean18.79657388
Median Absolute Deviation (MAD)19
Skewness0.1945329269
Sum8778
Variance267.6216191
MonotonicityNot monotonic
2022-11-22T08:28:57.301316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0153
 
17.0%
3058
 
6.5%
2053
 
5.9%
4051
 
5.7%
2526
 
2.9%
3517
 
1.9%
1515
 
1.7%
5013
 
1.4%
109
 
1.0%
56
 
0.7%
Other values (32)66
 
7.3%
(Missing)432
48.1%
ValueCountFrequency (%)
0153
17.0%
13
 
0.3%
21
 
0.1%
32
 
0.2%
43
 
0.3%
56
 
0.7%
63
 
0.3%
73
 
0.3%
82
 
0.2%
109
 
1.0%
ValueCountFrequency (%)
601
 
0.1%
542
 
0.2%
5013
 
1.4%
481
 
0.1%
473
 
0.3%
456
 
0.7%
421
 
0.1%
412
 
0.2%
4051
5.7%
382
 
0.2%

fbs
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing90
Missing (%)10.0%
Memory size51.0 KiB
0.0
674 
1.0
135 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0674
75.0%
1.0135
 
15.0%
(Missing)90
 
10.0%

Length

2022-11-22T08:28:57.373992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:57.436046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0674
83.3%
1.0135
 
16.7%

Most occurring characters

ValueCountFrequency (%)
01483
61.1%
.809
33.3%
1135
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1618
66.7%
Other Punctuation809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01483
91.7%
1135
 
8.3%
Other Punctuation
ValueCountFrequency (%)
.809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01483
61.1%
.809
33.3%
1135
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01483
61.1%
.809
33.3%
1135
 
5.6%

dm
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.1%
Missing804
Missing (%)89.4%
Memory size37.1 KiB
1.0
91 
0.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters285
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.091
 
10.1%
0.04
 
0.4%
(Missing)804
89.4%

Length

2022-11-22T08:28:57.488664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:57.542922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.091
95.8%
0.04
 
4.2%

Most occurring characters

ValueCountFrequency (%)
099
34.7%
.95
33.3%
191
31.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number190
66.7%
Other Punctuation95
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
099
52.1%
191
47.9%
Other Punctuation
ValueCountFrequency (%)
.95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
099
34.7%
.95
33.3%
191
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
099
34.7%
.95
33.3%
191
31.9%

famhist
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing422
Missing (%)46.9%
Memory size44.6 KiB
1.0
269 
0.0
208 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1431
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0269
29.9%
0.0208
23.1%
(Missing)422
46.9%

Length

2022-11-22T08:28:57.589276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:57.644625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0269
56.4%
0.0208
43.6%

Most occurring characters

ValueCountFrequency (%)
0685
47.9%
.477
33.3%
1269
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number954
66.7%
Other Punctuation477
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0685
71.8%
1269
 
28.2%
Other Punctuation
ValueCountFrequency (%)
.477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0685
47.9%
.477
33.3%
1269
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0685
47.9%
.477
33.3%
1269
 
18.8%

restecg
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size52.8 KiB
0.0
538 
2.0
182 
1.0
177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2691
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0538
59.8%
2.0182
 
20.2%
1.0177
 
19.7%
(Missing)2
 
0.2%

Length

2022-11-22T08:28:57.691417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:57.752745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0538
60.0%
2.0182
 
20.3%
1.0177
 
19.7%

Most occurring characters

ValueCountFrequency (%)
01435
53.3%
.897
33.3%
2182
 
6.8%
1177
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1794
66.7%
Other Punctuation897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01435
80.0%
2182
 
10.1%
1177
 
9.9%
Other Punctuation
ValueCountFrequency (%)
.897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01435
53.3%
.897
33.3%
2182
 
6.8%
1177
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01435
53.3%
.897
33.3%
2182
 
6.8%
1177
 
6.6%

dig
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing68
Missing (%)7.6%
Memory size51.5 KiB
0.0
802 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2493
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0802
89.2%
1.029
 
3.2%
(Missing)68
 
7.6%

Length

2022-11-22T08:28:57.809437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:57.871263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0802
96.5%
1.029
 
3.5%

Most occurring characters

ValueCountFrequency (%)
01633
65.5%
.831
33.3%
129
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1662
66.7%
Other Punctuation831
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01633
98.3%
129
 
1.7%
Other Punctuation
ValueCountFrequency (%)
.831
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2493
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01633
65.5%
.831
33.3%
129
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01633
65.5%
.831
33.3%
129
 
1.2%

prop
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.4%
Missing66
Missing (%)7.3%
Memory size51.5 KiB
0.0
618 
1.0
214 
22.0
 
1

Length

Max length4
Median length3
Mean length3.00120048
Min length3

Characters and Unicode

Total characters2500
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0618
68.7%
1.0214
 
23.8%
22.01
 
0.1%
(Missing)66
 
7.3%

Length

2022-11-22T08:28:57.924670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:57.989918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0618
74.2%
1.0214
 
25.7%
22.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01451
58.0%
.833
33.3%
1214
 
8.6%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1667
66.7%
Other Punctuation833
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01451
87.0%
1214
 
12.8%
22
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.833
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01451
58.0%
.833
33.3%
1214
 
8.6%
22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01451
58.0%
.833
33.3%
1214
 
8.6%
22
 
0.1%

nitr
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing65
Missing (%)7.2%
Memory size51.5 KiB
0.0
612 
1.0
222 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2502
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0612
68.1%
1.0222
 
24.7%
(Missing)65
 
7.2%

Length

2022-11-22T08:28:58.045389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:58.107697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0612
73.4%
1.0222
 
26.6%

Most occurring characters

ValueCountFrequency (%)
01446
57.8%
.834
33.3%
1222
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1668
66.7%
Other Punctuation834
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01446
86.7%
1222
 
13.3%
Other Punctuation
ValueCountFrequency (%)
.834
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2502
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01446
57.8%
.834
33.3%
1222
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01446
57.8%
.834
33.3%
1222
 
8.9%

pro
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing63
Missing (%)7.0%
Memory size51.6 KiB
0.0
692 
1.0
144 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2508
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0692
77.0%
1.0144
 
16.0%
(Missing)63
 
7.0%

Length

2022-11-22T08:28:58.160818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:58.222892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0692
82.8%
1.0144
 
17.2%

Most occurring characters

ValueCountFrequency (%)
01528
60.9%
.836
33.3%
1144
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1672
66.7%
Other Punctuation836
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01528
91.4%
1144
 
8.6%
Other Punctuation
ValueCountFrequency (%)
.836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01528
60.9%
.836
33.3%
1144
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01528
60.9%
.836
33.3%
1144
 
5.7%

diuretic
Categorical

MISSING

Distinct2
Distinct (%)0.2%
Missing82
Missing (%)9.1%
Memory size51.2 KiB
0.0
725 
1.0
92 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2451
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0725
80.6%
1.092
 
10.2%
(Missing)82
 
9.1%

Length

2022-11-22T08:28:58.275517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:28:58.537621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0725
88.7%
1.092
 
11.3%

Most occurring characters

ValueCountFrequency (%)
01542
62.9%
.817
33.3%
192
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1634
66.7%
Other Punctuation817
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01542
94.4%
192
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.817
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2451
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01542
62.9%
.817
33.3%
192
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01542
62.9%
.817
33.3%
192
 
3.8%

proto
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)1.8%
Missing112
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean37.08132147
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:58.585407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q375
95-th percentile128.5
Maximum200
Range199
Interquartile range (IQR)74

Descriptive statistics

Standard deviation50.14455942
Coefficient of variation (CV)1.352286203
Kurtosis0.01681618191
Mean37.08132147
Median Absolute Deviation (MAD)4
Skewness1.152180448
Sum29183
Variance2514.476839
MonotonicityNot monotonic
2022-11-22T08:28:58.639091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1324
36.0%
598
 
10.9%
7573
 
8.1%
1273
 
8.1%
10070
 
7.8%
12552
 
5.8%
5034
 
3.8%
15025
 
2.8%
2516
 
1.8%
17512
 
1.3%
Other values (4)10
 
1.1%
(Missing)112
 
12.5%
ValueCountFrequency (%)
1324
36.0%
42
 
0.2%
598
 
10.9%
65
 
0.6%
1273
 
8.1%
2516
 
1.8%
5034
 
3.8%
7573
 
8.1%
10070
 
7.8%
12552
 
5.8%
ValueCountFrequency (%)
2002
 
0.2%
17512
 
1.3%
15025
 
2.8%
1301
 
0.1%
12552
5.8%
10070
7.8%
7573
8.1%
5034
3.8%
2516
 
1.8%
1273
8.1%

thaldur
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct86
Distinct (%)10.2%
Missing56
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean8.655871886
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:58.707585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q16
median8.1
Q310.5
95-th percentile16
Maximum24
Range23
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.746617387
Coefficient of variation (CV)0.4328411322
Kurtosis0.8753847945
Mean8.655871886
Median Absolute Deviation (MAD)2.1
Skewness0.8045143204
Sum7296.9
Variance14.03714185
MonotonicityNot monotonic
2022-11-22T08:28:58.783446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993
 
10.3%
765
 
7.2%
660
 
6.7%
1051
 
5.7%
849
 
5.5%
1145
 
5.0%
1245
 
5.0%
439
 
4.3%
535
 
3.9%
1332
 
3.6%
Other values (76)329
36.6%
(Missing)56
 
6.2%
ValueCountFrequency (%)
11
 
0.1%
1.54
 
0.4%
1.71
 
0.1%
1.81
 
0.1%
211
1.2%
2.31
 
0.1%
2.51
 
0.1%
322
2.4%
3.12
 
0.2%
3.21
 
0.1%
ValueCountFrequency (%)
241
 
0.1%
211
 
0.1%
206
 
0.7%
1912
1.3%
1815
1.7%
175
 
0.6%
16.51
 
0.1%
166
 
0.7%
1511
1.2%
14.41
 
0.1%

thaltime
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct64
Distinct (%)14.3%
Missing453
Missing (%)50.4%
Infinite0
Infinite (%)0.0%
Mean5.690358744
Minimum0
Maximum20
Zeros70
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:58.863932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q38
95-th percentile12
Maximum20
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.99467301
Coefficient of variation (CV)0.7020072353
Kurtosis0.1721897174
Mean5.690358744
Median Absolute Deviation (MAD)3
Skewness0.5072290638
Sum2537.9
Variance15.95741246
MonotonicityNot monotonic
2022-11-22T08:28:58.945583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
070
 
7.8%
665
 
7.2%
357
 
6.3%
928
 
3.1%
820
 
2.2%
1019
 
2.1%
417
 
1.9%
1217
 
1.9%
516
 
1.8%
7.511
 
1.2%
Other values (54)126
 
14.0%
(Missing)453
50.4%
ValueCountFrequency (%)
070
7.8%
0.51
 
0.1%
0.71
 
0.1%
15
 
0.6%
1.53
 
0.3%
27
 
0.8%
2.52
 
0.2%
357
6.3%
3.55
 
0.6%
3.62
 
0.2%
ValueCountFrequency (%)
201
 
0.1%
192
0.2%
17.81
 
0.1%
17.51
 
0.1%
15.81
 
0.1%
153
0.3%
14.51
 
0.1%
14.31
 
0.1%
142
0.2%
13.51
 
0.1%

met
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)4.3%
Missing105
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean16.48312343
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:59.024310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q310
95-th percentile100
Maximum200
Range198
Interquartile range (IQR)5

Descriptive statistics

Standard deviation30.77280112
Coefficient of variation (CV)1.866927786
Kurtosis10.72913546
Mean16.48312343
Median Absolute Deviation (MAD)2
Skewness3.378510563
Sum13087.6
Variance946.9652886
MonotonicityNot monotonic
2022-11-22T08:28:59.093641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7107
11.9%
5106
11.8%
6102
11.3%
971
7.9%
1058
 
6.5%
454
 
6.0%
852
 
5.8%
331
 
3.4%
10031
 
3.4%
1327
 
3.0%
Other values (24)155
17.2%
(Missing)105
11.7%
ValueCountFrequency (%)
222
 
2.4%
2.51
 
0.1%
331
 
3.4%
3.51
 
0.1%
454
6.0%
4.51
 
0.1%
5106
11.8%
5.41
 
0.1%
5.81
 
0.1%
6102
11.3%
ValueCountFrequency (%)
2001
 
0.1%
15021
2.3%
1252
 
0.2%
10031
3.4%
757
 
0.8%
5011
 
1.2%
182
 
0.2%
172
 
0.2%
167
 
0.8%
153
 
0.3%

thalach
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct119
Distinct (%)14.1%
Missing55
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean137.2985782
Minimum60
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:59.170832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile95
Q1120
median140
Q3157
95-th percentile178
Maximum202
Range142
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.96595906
Coefficient of variation (CV)0.1891203784
Kurtosis-0.4847315481
Mean137.2985782
Median Absolute Deviation (MAD)20
Skewness-0.1999350826
Sum115880
Variance674.23103
MonotonicityNot monotonic
2022-11-22T08:28:59.248274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15042
 
4.7%
14040
 
4.4%
12035
 
3.9%
13029
 
3.2%
16026
 
2.9%
11021
 
2.3%
12520
 
2.2%
17020
 
2.2%
12216
 
1.8%
10014
 
1.6%
Other values (109)581
64.6%
(Missing)55
 
6.1%
ValueCountFrequency (%)
601
0.1%
631
0.1%
671
0.1%
691
0.1%
701
0.1%
711
0.1%
722
0.2%
731
0.1%
771
0.1%
781
0.1%
ValueCountFrequency (%)
2021
 
0.1%
1951
 
0.1%
1941
 
0.1%
1921
 
0.1%
1902
0.2%
1882
0.2%
1871
 
0.1%
1862
0.2%
1854
0.4%
1844
0.4%

thalrest
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct75
Distinct (%)8.9%
Missing56
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean75.48754448
Minimum37
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:59.332091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile55
Q165
median74
Q384
95-th percentile100
Maximum139
Range102
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.72796148
Coefficient of variation (CV)0.1951045246
Kurtosis0.7390791357
Mean75.48754448
Median Absolute Deviation (MAD)10
Skewness0.6366775286
Sum63636
Variance216.9128494
MonotonicityNot monotonic
2022-11-22T08:28:59.413959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7041
 
4.6%
7433
 
3.7%
8030
 
3.3%
6830
 
3.3%
7527
 
3.0%
7226
 
2.9%
6426
 
2.9%
7325
 
2.8%
8425
 
2.8%
7824
 
2.7%
Other values (65)556
61.8%
(Missing)56
 
6.2%
ValueCountFrequency (%)
371
 
0.1%
391
 
0.1%
401
 
0.1%
431
 
0.1%
441
 
0.1%
462
0.2%
471
 
0.1%
494
0.4%
504
0.4%
511
 
0.1%
ValueCountFrequency (%)
1391
 
0.1%
1341
 
0.1%
1253
0.3%
1241
 
0.1%
1203
0.3%
1191
 
0.1%
1161
 
0.1%
1152
 
0.2%
1122
 
0.2%
1106
0.7%

tpeakbps
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct74
Distinct (%)8.9%
Missing63
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean171.6411483
Minimum84
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:59.494451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile130
Q1155
median170
Q3190
95-th percentile220
Maximum240
Range156
Interquartile range (IQR)35

Descriptive statistics

Standard deviation25.73448825
Coefficient of variation (CV)0.1499319278
Kurtosis0.1626878493
Mean171.6411483
Median Absolute Deviation (MAD)18
Skewness0.04005466243
Sum143492
Variance662.2638856
MonotonicityNot monotonic
2022-11-22T08:28:59.572738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18097
 
10.8%
16095
 
10.6%
17081
 
9.0%
19066
 
7.3%
20058
 
6.5%
15047
 
5.2%
14040
 
4.4%
22024
 
2.7%
21021
 
2.3%
13018
 
2.0%
Other values (64)289
32.1%
(Missing)63
 
7.0%
ValueCountFrequency (%)
841
 
0.1%
901
 
0.1%
921
 
0.1%
982
 
0.2%
1001
 
0.1%
1104
0.4%
1121
 
0.1%
1151
 
0.1%
1161
 
0.1%
1209
1.0%
ValueCountFrequency (%)
2405
 
0.6%
2351
 
0.1%
2321
 
0.1%
23014
1.6%
2281
 
0.1%
2241
 
0.1%
22024
2.7%
2161
 
0.1%
2155
 
0.6%
21021
2.3%

tpeakbpd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct51
Distinct (%)6.1%
Missing63
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean87.2930622
Minimum11
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:59.655196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile65
Q180
median88
Q3100
95-th percentile110
Maximum134
Range123
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.73458597
Coefficient of variation (CV)0.1687944677
Kurtosis0.924026069
Mean87.2930622
Median Absolute Deviation (MAD)10
Skewness-0.1306504115
Sum72977
Variance217.1080237
MonotonicityNot monotonic
2022-11-22T08:28:59.737463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80162
18.0%
90131
14.6%
100114
12.7%
7059
 
6.6%
11043
 
4.8%
9528
 
3.1%
8525
 
2.8%
6023
 
2.6%
7523
 
2.6%
7822
 
2.4%
Other values (41)206
22.9%
(Missing)63
 
7.0%
ValueCountFrequency (%)
111
 
0.1%
261
 
0.1%
402
 
0.2%
451
 
0.1%
502
 
0.2%
551
 
0.1%
562
 
0.2%
583
 
0.3%
6023
2.6%
623
 
0.3%
ValueCountFrequency (%)
1341
 
0.1%
1302
 
0.2%
12015
 
1.7%
1184
 
0.4%
1162
 
0.2%
1158
 
0.9%
1142
 
0.2%
1121
 
0.1%
11043
4.8%
1082
 
0.2%

trestbpd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)4.0%
Missing59
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean83.52380952
Minimum0
Maximum120
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:28:59.812194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q180
median80
Q390
95-th percentile100
Maximum120
Range120
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.25256291
Coefficient of variation (CV)0.1227501831
Kurtosis4.985306216
Mean83.52380952
Median Absolute Deviation (MAD)8
Skewness-0.5407270172
Sum70160
Variance105.1150463
MonotonicityNot monotonic
2022-11-22T08:28:59.883828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
80258
28.7%
90158
17.6%
7088
 
9.8%
10064
 
7.1%
8542
 
4.7%
7824
 
2.7%
9523
 
2.6%
7521
 
2.3%
8215
 
1.7%
8814
 
1.6%
Other values (24)133
14.8%
(Missing)59
 
6.6%
ValueCountFrequency (%)
01
 
0.1%
502
 
0.2%
581
 
0.1%
6012
 
1.3%
644
 
0.4%
656
 
0.7%
661
 
0.1%
684
 
0.4%
7088
9.8%
728
 
0.9%
ValueCountFrequency (%)
1201
 
0.1%
1107
 
0.8%
1062
 
0.2%
1055
 
0.6%
1041
 
0.1%
1021
 
0.1%
10064
7.1%
9812
 
1.3%
967
 
0.8%
9523
 
2.6%

exang
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing55
Missing (%)6.1%
Memory size51.7 KiB
0.0
514 
1.0
330 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2532
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0514
57.2%
1.0330
36.7%
(Missing)55
 
6.1%

Length

2022-11-22T08:28:59.950954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:00.013266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0514
60.9%
1.0330
39.1%

Most occurring characters

ValueCountFrequency (%)
01358
53.6%
.844
33.3%
1330
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1688
66.7%
Other Punctuation844
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01358
80.5%
1330
 
19.5%
Other Punctuation
ValueCountFrequency (%)
.844
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2532
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01358
53.6%
.844
33.3%
1330
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01358
53.6%
.844
33.3%
1330
 
13.0%

xhypo
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing58
Missing (%)6.5%
Memory size51.7 KiB
0.0
819 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2523
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0819
91.1%
1.022
 
2.4%
(Missing)58
 
6.5%

Length

2022-11-22T08:29:00.066902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:00.128748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0819
97.4%
1.022
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01660
65.8%
.841
33.3%
122
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1682
66.7%
Other Punctuation841
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01660
98.7%
122
 
1.3%
Other Punctuation
ValueCountFrequency (%)
.841
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2523
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01660
65.8%
.841
33.3%
122
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2523
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01660
65.8%
.841
33.3%
122
 
0.9%

oldpeak
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct52
Distinct (%)6.2%
Missing62
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean0.8704898447
Minimum-2.6
Maximum6.2
Zeros362
Zeros (%)40.3%
Negative12
Negative (%)1.3%
Memory size7.1 KiB
2022-11-22T08:29:00.191482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile0
Q10
median0.5
Q31.5
95-th percentile3
Maximum6.2
Range8.8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.080548494
Coefficient of variation (CV)1.241310855
Kurtosis1.147635071
Mean0.8704898447
Median Absolute Deviation (MAD)0.5
Skewness1.027733086
Sum728.6
Variance1.167585047
MonotonicityNot monotonic
2022-11-22T08:29:00.269363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0362
40.3%
182
 
9.1%
275
 
8.3%
1.548
 
5.3%
328
 
3.1%
0.519
 
2.1%
2.516
 
1.8%
1.415
 
1.7%
0.614
 
1.6%
0.814
 
1.6%
Other values (42)164
18.2%
(Missing)62
 
6.9%
ValueCountFrequency (%)
-2.61
0.1%
-21
0.1%
-1.51
0.1%
-1.11
0.1%
-12
0.2%
-0.91
0.1%
-0.81
0.1%
-0.71
0.1%
-0.52
0.2%
-0.11
0.1%
ValueCountFrequency (%)
6.21
 
0.1%
5.61
 
0.1%
51
 
0.1%
4.22
 
0.2%
47
0.8%
3.81
 
0.1%
3.71
 
0.1%
3.64
0.4%
3.52
 
0.2%
3.42
 
0.2%

slope
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.7%
Missing308
Missing (%)34.3%
Memory size46.8 KiB
2.0
334 
1.0
196 
3.0
60 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0334
37.2%
1.0196
21.8%
3.060
 
6.7%
0.01
 
0.1%
(Missing)308
34.3%

Length

2022-11-22T08:29:00.338444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:00.399205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0334
56.5%
1.0196
33.2%
3.060
 
10.2%
0.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0592
33.4%
.591
33.3%
2334
18.8%
1196
 
11.1%
360
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1182
66.7%
Other Punctuation591
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0592
50.1%
2334
28.3%
1196
 
16.6%
360
 
5.1%
Other Punctuation
ValueCountFrequency (%)
.591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0592
33.4%
.591
33.3%
2334
18.8%
1196
 
11.1%
360
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0592
33.4%
.591
33.3%
2334
18.8%
1196
 
11.1%
360
 
3.4%

rldv5
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct31
Distinct (%)6.5%
Missing425
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean14.39873418
Minimum2
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:29:00.457057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q110
median14
Q318
95-th percentile25
Maximum36
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.70294224
Coefficient of variation (CV)0.396072472
Kurtosis0.02389652918
Mean14.39873418
Median Absolute Deviation (MAD)4
Skewness0.4842962135
Sum6825
Variance32.52355019
MonotonicityNot monotonic
2022-11-22T08:29:00.521689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1042
 
4.7%
1134
 
3.8%
1234
 
3.8%
1433
 
3.7%
1333
 
3.7%
1530
 
3.3%
1727
 
3.0%
2027
 
3.0%
1822
 
2.4%
1920
 
2.2%
Other values (21)172
19.1%
(Missing)425
47.3%
ValueCountFrequency (%)
21
 
0.1%
31
 
0.1%
49
 
1.0%
59
 
1.0%
612
 
1.3%
716
 
1.8%
819
2.1%
920
2.2%
1042
4.7%
1134
3.8%
ValueCountFrequency (%)
361
 
0.1%
312
 
0.2%
301
 
0.1%
291
 
0.1%
283
 
0.3%
274
 
0.4%
264
 
0.4%
2511
1.2%
2410
1.1%
237
0.8%

rldv5e
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct141
Distinct (%)18.6%
Missing142
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean54.91413474
Minimum2
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:29:00.597927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q112
median19
Q3102
95-th percentile176
Maximum270
Range268
Interquartile range (IQR)90

Descriptive statistics

Standard deviation60.309425
Coefficient of variation (CV)1.098249572
Kurtosis0.2720851797
Mean54.91413474
Median Absolute Deviation (MAD)10
Skewness1.185535476
Sum41570
Variance3637.226744
MonotonicityNot monotonic
2022-11-22T08:29:00.839950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2042
 
4.7%
1138
 
4.2%
1533
 
3.7%
1032
 
3.6%
1330
 
3.3%
1730
 
3.3%
927
 
3.0%
1826
 
2.9%
1226
 
2.9%
823
 
2.6%
Other values (131)450
50.1%
(Missing)142
 
15.8%
ValueCountFrequency (%)
21
 
0.1%
33
 
0.3%
47
 
0.8%
56
 
0.7%
618
2.0%
723
2.6%
823
2.6%
927
3.0%
1032
3.6%
1138
4.2%
ValueCountFrequency (%)
2701
 
0.1%
2531
 
0.1%
2521
 
0.1%
2401
 
0.1%
2311
 
0.1%
2302
0.2%
2271
 
0.1%
2251
 
0.1%
2221
 
0.1%
2203
0.3%

ca
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing608
Missing (%)67.6%
Memory size40.9 KiB
0.0
171 
1.0
63 
2.0
37 
3.0
19 
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters873
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row9.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0171
 
19.0%
1.063
 
7.0%
2.037
 
4.1%
3.019
 
2.1%
9.01
 
0.1%
(Missing)608
67.6%

Length

2022-11-22T08:29:00.910492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:00.972568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0171
58.8%
1.063
 
21.6%
2.037
 
12.7%
3.019
 
6.5%
9.01
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0462
52.9%
.291
33.3%
163
 
7.2%
237
 
4.2%
319
 
2.2%
91
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number582
66.7%
Other Punctuation291
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0462
79.4%
163
 
10.8%
237
 
6.4%
319
 
3.3%
91
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.291
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common873
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0462
52.9%
.291
33.3%
163
 
7.2%
237
 
4.2%
319
 
2.2%
91
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0462
52.9%
.291
33.3%
163
 
7.2%
237
 
4.2%
319
 
2.2%
91
 
0.1%

restef
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)85.7%
Missing871
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean0.5310714286
Minimum0.22
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:29:01.032125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.3105
Q10.4075
median0.57
Q30.625
95-th percentile0.746
Maximum0.8
Range0.58
Interquartile range (IQR)0.2175

Descriptive statistics

Standard deviation0.1461946771
Coefficient of variation (CV)0.2752825123
Kurtosis-0.6173807538
Mean0.5310714286
Median Absolute Deviation (MAD)0.11
Skewness-0.2094493985
Sum14.87
Variance0.0213728836
MonotonicityNot monotonic
2022-11-22T08:29:01.097324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.592
 
0.2%
0.452
 
0.2%
0.642
 
0.2%
0.62
 
0.2%
0.51
 
0.1%
0.221
 
0.1%
0.411
 
0.1%
0.71
 
0.1%
0.581
 
0.1%
0.761
 
0.1%
Other values (14)14
 
1.6%
(Missing)871
96.9%
ValueCountFrequency (%)
0.221
0.1%
0.31
0.1%
0.331
0.1%
0.361
0.1%
0.381
0.1%
0.391
0.1%
0.41
0.1%
0.411
0.1%
0.452
0.2%
0.481
0.1%
ValueCountFrequency (%)
0.81
0.1%
0.761
0.1%
0.721
0.1%
0.71
0.1%
0.671
0.1%
0.642
0.2%
0.621
0.1%
0.611
0.1%
0.62
0.2%
0.592
0.2%

restwm
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)13.3%
Missing869
Missing (%)96.7%
Memory size35.8 KiB
0.0
13 
2.0
1.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.013
 
1.4%
2.08
 
0.9%
1.06
 
0.7%
3.03
 
0.3%
(Missing)869
96.7%

Length

2022-11-22T08:29:01.164253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:01.224615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.013
43.3%
2.08
26.7%
1.06
20.0%
3.03
 
10.0%

Most occurring characters

ValueCountFrequency (%)
043
47.8%
.30
33.3%
28
 
8.9%
16
 
6.7%
33
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number60
66.7%
Other Punctuation30
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
043
71.7%
28
 
13.3%
16
 
10.0%
33
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common90
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
043
47.8%
.30
33.3%
28
 
8.9%
16
 
6.7%
33
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
043
47.8%
.30
33.3%
28
 
8.9%
16
 
6.7%
33
 
3.3%

exeref
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing897
Missing (%)99.8%
Memory size35.3 KiB
0.6
0.5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.6
2nd row0.5

Common Values

ValueCountFrequency (%)
0.61
 
0.1%
0.51
 
0.1%
(Missing)897
99.8%

Length

2022-11-22T08:29:01.281870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:01.342782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.61
50.0%
0.51
50.0%

Most occurring characters

ValueCountFrequency (%)
02
33.3%
.2
33.3%
61
16.7%
51
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4
66.7%
Other Punctuation2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
50.0%
61
25.0%
51
25.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02
33.3%
.2
33.3%
61
16.7%
51
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02
33.3%
.2
33.3%
61
16.7%
51
16.7%

exerwm
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)40.0%
Missing894
Missing (%)99.4%
Memory size35.3 KiB
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04
 
0.4%
1.01
 
0.1%
(Missing)894
99.4%

Length

2022-11-22T08:29:01.392563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:01.448839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.04
80.0%
1.01
 
20.0%

Most occurring characters

ValueCountFrequency (%)
09
60.0%
.5
33.3%
11
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
66.7%
Other Punctuation5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09
90.0%
11
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09
60.0%
.5
33.3%
11
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09
60.0%
.5
33.3%
11
 
6.7%

thal
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)1.7%
Missing477
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean5.018957346
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:29:01.491472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.949388321
Coefficient of variation (CV)0.3884050384
Kurtosis-1.835218847
Mean5.018957346
Median Absolute Deviation (MAD)1
Skewness-0.125321007
Sum2118
Variance3.800114825
MonotonicityNot monotonic
2022-11-22T08:29:01.542094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3189
 
21.0%
7182
 
20.2%
642
 
4.7%
14
 
0.4%
53
 
0.3%
41
 
0.1%
21
 
0.1%
(Missing)477
53.1%
ValueCountFrequency (%)
14
 
0.4%
21
 
0.1%
3189
21.0%
41
 
0.1%
53
 
0.3%
642
 
4.7%
7182
20.2%
ValueCountFrequency (%)
7182
20.2%
642
 
4.7%
53
 
0.3%
41
 
0.1%
3189
21.0%
21
 
0.1%
14
 
0.4%

num
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
404 
1
191 
3
132 
2
130 
4
42 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row3
5th row0

Common Values

ValueCountFrequency (%)
0404
44.9%
1191
21.2%
3132
 
14.7%
2130
 
14.5%
442
 
4.7%

Length

2022-11-22T08:29:01.600673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:01.665475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0404
44.9%
1191
21.2%
3132
 
14.7%
2130
 
14.5%
442
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0404
44.9%
1191
21.2%
3132
 
14.7%
2130
 
14.5%
442
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0404
44.9%
1191
21.2%
3132
 
14.7%
2130
 
14.5%
442
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0404
44.9%
1191
21.2%
3132
 
14.7%
2130
 
14.5%
442
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0404
44.9%
1191
21.2%
3132
 
14.7%
2130
 
14.5%
442
 
4.7%

cathef
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct88
Distinct (%)28.3%
Missing588
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean27.62311897
Minimum0.22
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-11-22T08:29:01.736146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.475
Q10.685
median0.82
Q363
95-th percentile73
Maximum86
Range85.78
Interquartile range (IQR)62.315

Descriptive statistics

Standard deviation31.67529475
Coefficient of variation (CV)1.146695085
Kurtosis-1.742548977
Mean27.62311897
Median Absolute Deviation (MAD)0.32
Skewness0.3887194199
Sum8590.79
Variance1003.324298
MonotonicityNot monotonic
2022-11-22T08:29:01.813588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6710
 
1.1%
709
 
1.0%
0.89
 
1.0%
0.739
 
1.0%
0.748
 
0.9%
688
 
0.9%
0.718
 
0.9%
658
 
0.9%
0.77
 
0.8%
637
 
0.8%
Other values (78)228
 
25.4%
(Missing)588
65.4%
ValueCountFrequency (%)
0.222
 
0.2%
0.321
 
0.1%
0.45
0.6%
0.412
 
0.2%
0.421
 
0.1%
0.432
 
0.2%
0.441
 
0.1%
0.451
 
0.1%
0.471
 
0.1%
0.483
0.3%
ValueCountFrequency (%)
861
 
0.1%
831
 
0.1%
801
 
0.1%
791
 
0.1%
772
0.2%
764
0.4%
754
0.4%
741
 
0.1%
733
0.3%
723
0.3%

dataset
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.1 KiB
hungarian
294 
cleveland
282 
long-beach-va
200 
switzerland
123 

Length

Max length13
Median length9
Mean length10.16351502
Min length9

Characters and Unicode

Total characters9137
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhungarian
2nd rowhungarian
3rd rowhungarian
4th rowhungarian
5th rowhungarian

Common Values

ValueCountFrequency (%)
hungarian294
32.7%
cleveland282
31.4%
long-beach-va200
22.2%
switzerland123
13.7%

Length

2022-11-22T08:29:01.888438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-22T08:29:01.960375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
hungarian294
32.7%
cleveland282
31.4%
long-beach-va200
22.2%
switzerland123
13.7%

Most occurring characters

ValueCountFrequency (%)
a1393
15.2%
n1193
13.1%
e887
9.7%
l887
9.7%
h494
 
5.4%
g494
 
5.4%
c482
 
5.3%
v482
 
5.3%
r417
 
4.6%
i417
 
4.6%
Other values (9)1991
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8737
95.6%
Dash Punctuation400
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1393
15.9%
n1193
13.7%
e887
10.2%
l887
10.2%
h494
 
5.7%
g494
 
5.7%
c482
 
5.5%
v482
 
5.5%
r417
 
4.8%
i417
 
4.8%
Other values (8)1591
18.2%
Dash Punctuation
ValueCountFrequency (%)
-400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8737
95.6%
Common400
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1393
15.9%
n1193
13.7%
e887
10.2%
l887
10.2%
h494
 
5.7%
g494
 
5.7%
c482
 
5.5%
v482
 
5.5%
r417
 
4.8%
i417
 
4.8%
Other values (8)1591
18.2%
Common
ValueCountFrequency (%)
-400
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1393
15.2%
n1193
13.1%
e887
9.7%
l887
9.7%
h494
 
5.4%
g494
 
5.4%
c482
 
5.3%
v482
 
5.3%
r417
 
4.6%
i417
 
4.6%
Other values (9)1991
21.8%

Interactions

2022-11-22T08:28:52.813926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:26.947249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:28.353098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:29.828525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:31.149065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:32.524691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:33.717381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:35.229938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:36.577595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:37.870201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:39.365758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:40.682220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:42.124203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:43.506873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:45.010496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:46.348946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:47.780124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:48.993108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:50.401231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:51.504958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:52.865302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:27.161866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:28.420931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:31.207366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:32.583594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:33.786036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:36.636974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:37.935495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:39.430649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:40.746224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:51.561731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:52.921957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:35.369684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:28.758090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:30.234642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:41.293260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:46.803380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:48.215219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:49.435082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:50.787292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:51.915155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:53.261810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:27.593545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:28.889271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:30.366077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:31.639844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:33.021184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:34.258749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:35.768749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:37.087501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:38.573369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:39.895357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:41.357099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:42.679061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:44.224311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-22T08:28:47.662302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:48.887359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:50.289349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:51.386432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:52.548444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:53.873116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:28.300224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:29.771793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:31.094215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:32.461250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:33.660635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:35.173068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:36.510405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:37.815997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:39.310701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:40.628058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:42.069701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:43.449080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:44.957948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:46.293403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:47.722524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:48.941438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:50.347135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:51.445757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-22T08:28:52.607055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-22T08:29:02.058910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-22T08:29:02.263948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-22T08:29:02.466641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-22T08:29:02.670283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-22T08:28:54.012436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-22T08:28:54.559045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-22T08:28:54.903074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-22T08:28:55.659528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

agesexpainlocpainexerrelrestcptrestbpshtncholsmokecigsyearsfbsdmfamhistrestecgdigpropnitrprodiureticprotothaldurthaltimemetthalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeaksloperldv5rldv5ecarestefrestwmexerefexerwmthalnumcathefdataset
04011.00.00.02140.00.0289.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0150.018.0NaN7.0172.086.0200.0110.086.00.00.00.0NaN26.020.0NaNNaNNaNNaNNaNNaN0NaNhungarian
14901.00.00.03160.01.0180.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0NaN10.09.07.0156.0100.0220.0106.090.00.00.01.02.014.013.0NaNNaNNaNNaNNaNNaN1NaNhungarian
23711.00.00.02130.00.0283.0NaNNaNNaN0.0NaNNaN1.00.00.00.00.00.0100.010.0NaN5.098.058.0180.0100.080.00.00.00.0NaN17.014.0NaNNaNNaNNaNNaNNaN0NaNhungarian
34801.01.01.04138.00.0214.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.050.05.04.04.0108.054.0210.0106.086.01.00.01.52.019.022.0NaNNaNNaNNaNNaNNaN3NaNhungarian
45411.00.01.03150.00.0NaNNaNNaNNaN0.0NaNNaN0.00.00.01.01.00.025.02.0NaN3.0122.074.0130.0100.090.00.01.00.0NaN13.09.0NaNNaNNaNNaNNaNNaN0NaNhungarian
53911.00.01.03120.00.0339.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0175.019.0NaN8.0170.086.0198.0100.080.00.00.00.0NaN20.021.0NaNNaNNaNNaNNaNNaN0NaNhungarian
64500.01.00.02130.00.0237.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0100.010.0NaN10.0170.090.0200.0106.084.00.00.00.0NaN11.011.0NaNNaNNaNNaNNaNNaN0NaNhungarian
75411.00.00.02110.00.0208.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0175.019.0NaN7.0142.056.0220.070.070.00.00.00.0NaN11.011.0NaNNaNNaNNaNNaNNaN0NaNhungarian
83711.01.01.04140.01.0207.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0125.015.013.57.0130.063.0190.0100.080.01.00.01.52.018.019.0NaNNaNNaNNaNNaNNaN1NaNhungarian
94801.00.00.02120.00.0284.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.075.07.0NaN4.0120.072.0140.080.080.00.00.00.0NaN6.06.0NaNNaNNaNNaNNaNNaN0NaNhungarian

Last rows

agesexpainlocpainexerrelrestcptrestbpshtncholsmokecigsyearsfbsdmfamhistrestecgdigpropnitrprodiureticprotothaldurthaltimemetthalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeaksloperldv5rldv5ecarestefrestwmexerefexerwmthalnumcathefdataset
8895101.01.01.04114.01.0258.00.00.00.01.01.01.02.00.01.00.00.00.01.04.0NaN5.096.052.0140.096.074.00.00.01.01.019.020.0NaNNaNNaNNaNNaNNaN00.74long-beach-va
8906211.01.01.04160.01.0254.01.040.047.01.0NaN1.01.01.00.01.01.01.05.03.5NaN2.5108.069.0160.090.080.01.00.03.02.020.019.0NaNNaNNaNNaNNaNNaN40.54long-beach-va
8915311.01.01.04144.01.0300.00.020.010.01.0NaN1.01.00.00.01.00.00.01.04.03.05.0128.076.0150.0102.094.01.00.01.52.012.013.0NaNNaNNaNNaNNaNNaN30.76long-beach-va
8926211.01.01.04158.01.0170.01.020.020.00.0NaN1.01.00.022.01.00.01.05.08.0NaN8.0138.086.0202.098.090.01.00.00.0NaN20.022.0NaNNaNNaNNaNNaNNaN1NaNlong-beach-va
8934611.01.01.04134.01.0310.01.020.021.00.0NaN1.00.00.00.00.00.00.01.05.5NaN7.0126.088.0174.0114.090.00.00.00.0NaN9.07.0NaNNaNNaNNaNNaN3.020.87long-beach-va
8945401.01.01.04127.00.0333.00.00.00.01.0NaN1.01.00.01.01.00.00.01.07.5NaN8.0154.083.0158.084.078.00.00.00.0NaN20.020.0NaNNaNNaNNaNNaNNaN10.76long-beach-va
8956210.00.00.01NaN0.0139.01.015.030.00.0NaN0.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.411.0NaNNaNNaN00.62long-beach-va
8965511.01.01.04122.01.0223.01.020.040.01.0NaN0.01.00.01.01.00.01.05.05.3NaN5.0100.074.0210.0100.070.00.00.00.0NaN6.04.0NaN0.393.0NaNNaN6.020.69long-beach-va
8975811.01.01.04NaN0.0385.00.010.020.01.01.01.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00.81long-beach-va
8986211.00.00.02120.01.0254.00.00.00.00.0NaN0.02.00.01.00.00.00.01.06.7NaN7.093.067.0164.0110.080.01.00.00.0NaN21.017.0NaNNaNNaNNaNNaNNaN1NaNlong-beach-va